Evidence of a large current of transcranial alternating current stimulation directly to deep brain regions

Entrainment by transcranial alternating current stimulation: Insights from models of cortical oscillations and dynamical systems theory

Signature of neuronal oscillations can be found in nearly every brain function. However, abnormal oscillatory activity is linked with several brain disorders. Transcranial alternating current stimulation (tACS) is a non-invasive brain stimulation technique that can potentially modulate neuronal oscillations and influence behavior both in health and disease. Yet, a complete understanding of how interacting networks of neurons are affected by tACS remains elusive. Entrainment effects by which tACS synchronizes neuronal oscillations is one of the main hypothesized mechanisms, as evidenced in animals and humans. Computational models of cortical oscillations may shed light on the entrainment effects of tACS, but current modeling studies lack specific guidelines to inform experimental investigations. This study addresses the existing gap in understanding the mechanisms of tACS effects on rhythmogenesis within the brain by providing a comprehensive overview of both theoretical and experimental perspectives. We explore the intricate interactions between oscillators and periodic stimulation through the lens of dynamical systems theory. Subsequently, we present a synthesis of experimental findings that demonstrate the effects of tACS on both individual neurons and collective oscillatory patterns in animal models and humans. Our review extends to computational investigations that elucidate the interplay between tACS and neuronal dynamics across diverse cortical network models. To illustrate these concepts, we conclude with a simple oscillatory neuron model, showcasing how fundamental theories of oscillatory behavior derived from dynamical systems, such as phase response of neurons to external perturbation, can account for the entrainment effects observed with tACS. Studies reviewed here render the necessity of integrated experimental and computational approaches for effective neuromodulation by tACS in health and disease.

The Latest Advances in Non-Invasive Neurostimulation for Insomnia: A Review

Insomnia has become a serious public health problem. Because sedative-hypnotics may cause cognitive and psychomotor impairment, serious adverse events, and long-term dependence, it is still controversial whether sleep medications are the first choice for patients with insomnia. Cognitive-behavioral therapy for sleep is limited by poor access, high cost, and poor response in some patients. With the development of sleep neuroscience, physical therapy has shown its advantages in treating insomnia. It can not only avoid the adverse reactions of drugs, but also make up for the lack of standardization of sleep cognitive behavioral therapy. In particular, non-invasive nerve stimulation technology has been increasingly used in the clinical treatment of insomnia in recent years. However, the public lacks understanding of the technology type, principle and efficacy of this treatment strategy, and the research reviews are insufficient, which may limit the choice of insomnia treatment for sleep workers. Therefore, we reviewed the latest research on non-invasive neurostimulation techniques for insomnia in recent years, mainly focusing on virtual reality, biofeedback, transcranial magnetic stimulation, transcranial electrical stimulation, transcutaneous auricular vagus nerve stimulation and other novel neuromodulation techniques, with the aim of providing more options for insomnia treatment and looking forward to the future development of sleep science.

Optimized Temporal Interference Stimulation Based on Convex Optimization: A Computational Study

Temporal interference (TI) stimulation is a non-invasive method targeting deep brain regions by applying two pairs of high-frequency currents with a slight frequency difference to the scalp. However, optimizing electrode configurations for TI via computational modeling is challenging and time-consuming due to the non-convex nature of the optimization. We propose a convex optimization-based method (CVXTI) for optimizing TI electrode configurations. We decompose the TI optimization into two convex steps, enabling rapid determination of electrode pair configurations. CVXTI accommodates various optimization objectives by incorporating different objective functions, thereby enhancing the focality of the stimulation field. Performance analysis of CVXTI shows superior results compared to other methods, particularly in deep brain regions. Subject variability analysis on four individuals highlights the necessity of customized stimulus optimization. CVXTI leverages the distribution characteristics of the TI envelope electric field to optimize electrode configurations, enhancing the optimization efficiency.

The role of 15 mA and 77.5 Hz transcranial alternating current stimulation in blood pressure regulation: A post hoc analysis of a randomized controlled trial

BACKGROUND

Transcranial alternating current stimulation (tACS) at 77.5 Hz and 15 mA, targeting the forehead and mastoid areas, has proven efficacious in patients with major depressive disorder (MDD) by simultaneously stimulating multiple brain nuclei and regions, many of which are critical for blood pressure regulation. This post hoc analysis aimed to assess the potential blood pressure-lowering effects of 77.5 Hz, 15 mA tACS in first-episode drug-naive MDD patients with normotension.

METHODS

Data from a previous randomized controlled trial (RCT) involving first-episode drug-naive MDD patients were analyzed. Participants underwent 20 sessions of either active tACS or sham stimulation. Vital signs, including systolic blood pressure (SBP) and diastolic blood pressure (DBP), were measured at baseline, after treatment (Week 4), and at follow-up (Week 8). Multivariate linear regression and Generalized Estimating Equations (GEE) models were used to evaluate the effects of the treatment on blood pressure.

RESULTS

Totally 68 participants were analysis (33 in the sham group and 35 in the active group). By Week 4, the active tACS group exhibited a significant reduction in both SBP and DBP compared to the sham group (coefficient - 2.04, 95 % CI -3.01 to -1.07, p < 0.001 on SBP, and coefficient - 1.92, 95 % CI -2.69 to -1.18, p < 0.001 on DBP).

CONCLUSIONS

tACS at 77.5 Hz and 15 mA can effectively reduce SBP and DBP in first-episode drug-naive depressive individuals with normotension, with greater reductions observed in those with higher baseline levels.

Application and research progress of different frequency tACS in stroke rehabilitation: A systematic review

After a stroke, abnormal changes in neural oscillations that are related to the severity and prognosis of the disease can occur. Resetting these abnormal neural oscillations is a potential approach for stroke rehabilitation. Transcranial alternating current stimulation (tACS) can modulate intrinsic neural oscillations noninvasively and has attracted attention as a possible technique to improve multiple post-stroke symptoms, including deficits in speech, vision, and motor ability and overall neurological recovery. The clinical effect of tACS varies according to the selected frequency. Therefore, choosing an appropriate frequency to optimize outcomes for specific dysfunctions is essential. This review focuses on the current research status and possibilities of tACS with different frequencies in stroke rehabilitation. We also discuss the possible mechanisms of tACS in stroke to provide a theoretical foundation for the method and highlight the controversial aspects that need further exploration. Although tACS has great potential, few clinical studies have applied it in the treatment of stroke, and no consensus has been reached. We analyze limitations in experimental designs and identify potential tACS approaches worthy of exploration in the future.

Somatosensory-Thalamic Functional Dysconnectivity Associated With Poststroke Motor Function Rehabilitation: A Resting-State fMRI Study

BACKGROUND

The thalamus plays a pivotal role in functional brain networks, yet its contribution to motor function recovery following stroke remains elusive. We aim to explore changes in thalamocortical functional connectivity poststroke and its correlation with motor function.

METHODS

Thirty-nine subacute ischemic stroke patients and 32 healthy individuals underwent resting-state functional magnetic resonance imaging (MRI). The Fugl-Meyer Assessment (FMA) was employed to evaluate upper and lower extremity motor function before and 1 year after stroke rehabilitation. The ipsilesional thalamus and contralesional thalamus were parceled into functional regions of interest (ROIs) based on connectivity with six cortical ROIs: prefrontal, motor, temporal, posterior parietal, somatosensory, and occipital cortex. Functional connectivity between each cortical ROI and its corresponding thalamic ROI was calculated and compared between groups. Differences identified in the ROI-to-ROI analysis were further investigated through seed-to-voxel whole-brain connectivity analyses to pinpoint thalamic dysconnectivity. Correlations with upper and lower extremity motor function were also analyzed.

RESULTS

Significant changes in thalamocortical functional connectivity were observed after stroke in ROI-to-ROI analysis, with bilateral somatosensory-thalamic connectivity decreased and ipsilesional temporal-thalamic and bilateral occipital-thalamic connectivity increased. Seed-to-voxel analysis localized ipsilesional thalamic hypoconnectivity to the ipsilesional rolandic operculum and ipsilesional precentral gyrus. Ipsilesional somatosensory-thalamic connectivity was positively correlated with baseline upper extremity FMA scores and negatively correlated with upper extremity motor function change rate at 1-year postdischarge.

CONCLUSIONS

This study provides new insights into the role of the thalamus in motor function recovery after stroke, offering preliminary evidence for its potential as a therapeutic target in poststroke rehabilitation.

Recent advances in nanotechnology for Parkinson's disease: diagnosis, treatment, and future perspectives

Parkinson's disease is a progressive neurodegenerative disease that destroys substantia nigra dopaminergic neurons, causing tremors, bradykinesia, rigidity, and postural instability. Current treatment approaches primarily focus on symptom management, employing pharmacological, non-pharmacological, and surgical methods. However, these treatments often result in fluctuating symptoms, side effects, and disease progression. Here, the authors have reviewed the emerging field of nanomedicine as a promising path for Parkinson's disease treatment, emphasizing its potential to overcome the limitations of traditional therapies. Nanomedicine utilizes nanoparticles for targeted drug delivery, leveraging their small size and high surface area to volume ratio to cross the blood-brain barrier and deliver therapeutic agents directly to affected brain regions. Various nanoparticles, including lipid-based, polymeric, metallic, and carbon-based, have shown potential in Parkinson's disease treatment. Additionally, nanocarrier systems like liposomes, nanogels, dendrimers, and solid lipid nanoparticles offer controlled and sustained release of therapeutic agents, enhancing their bioavailability and reducing side effects. This review provides insights into the pathophysiology of Parkinson's disease, highlighting the mechanisms of neurodegeneration, the role of alpha-synuclein, and the disruption of dopaminergic pathways. It further discusses the application of gene therapy in conjunction with nanomedicine for targeted therapeutic interventions.

Frequency-specific and state-dependent neural responses to brain stimulation

Non-invasive brain stimulation is promising for treating many neuropsychiatric and neurological conditions. It could be optimized by understanding its intracranial responses in different brain regions. We implanted multi-site intracranial electrodes and systematically assessed the acute responses in these regions to transcranial alternating current stimulation (tACS) at different frequencies. We observed robust neural oscillation changes in the hippocampus and amygdala in response to non-invasive tACS procedures, and these effects were frequency-specific and state-dependent. Notably, the hippocampus responded most strongly and stably to 10 Hz stimulation, with pronounced changes across a wide frequency range, suggesting the potential of 10 Hz oscillatory stimulation to modulate a broad range of neural activity related to cognitive functions. Future work with increased sample sizes is required to determine the clinical implications of these findings for therapeutic efficiency.

The safety and efficacy of applying a high-current temporal interference electrical stimulation in humans

Background

Temporal interference electrical stimulation (TI) is promise in targeting deep brain regions focally. However, limited electric field intensity challenges its efficacy.

Objective

This study aimed to introduce a high-current TI electrical stimulation protocol to enhance its intensity and evaluate its safety and efficacy when applied to the primary motor cortex (M1) in the human brain.

Methods

Safety assessments included a battery of biochemical and neuropsychological tests (NSE, MoCA, PPT, VAMS-R, and SAS measurements), 5-min resting-state electroencephalography (EEG) recordings before and after 30-min high-current TI electrical stimulation sessions (20 Hz, 70 Hz, sham). Adverse reactions were also documented post-stimulation. Efficacy evaluations involved two motor tasks, the simple reaction time (SRT) task and the one-increment task, to investigate the distinct contributions of beta (20 Hz) and gamma (70 Hz) oscillations to motor functions.

Results

Biochemical and neuropsychological tests revealed no significant differences between the groups. Additionally, no epileptic activities were detected in the EEG recordings. In the one-increment task, 20 Hz stimulation delayed participants' reaction time compared to the 70 Hz and sham groups. Conversely, in the SRT task, 70 Hz stimulation exhibited a tendency to enhance participants' performance relative to the sham group.

Conclusion

The proposed high-current TI electrical stimulation is both safe and effective for stimulating the human brain. Moreover, the distinct effects observed in motor tasks underscore the dissociative roles of beta and gamma oscillations in motor functions, offering valuable insights into the potential applications of high-current TI electrical stimulation in brain stimulation research.

Transcranial Alternating Current Stimulation: A Novel Neuromodulatory Treatment for Anxiety and Related Disorders

Given the high prevalence and socioeconomic burden of anxiety disorders, there is an urgent need for effective, fast-acting, cost-efficient treatment alternatives to traditional psychotherapy. Transcranial alternating current stimulation (tACS), a noninvasive brain stimulation technique that modulates endogenous brain oscillations via the application of sinusoidal currents to the scalp, emerges as a promising neuromodulatory treatment. We reviewed oscillatory neuropsychopathology in anxiety, examined current evidence of tACS interventions for anxiety and related disorders, and proposed novel simulation targets and protocols. We emphasize the need for rigorously designed clinical trials to systematically investigate the neuropsychological effects of different tACS protocols on diverse populations with pathological anxiety.

TRanscranial AlterNating current stimulation FOR patients with mild Alzheimer's Disease (TRANSFORM-AD): a randomized controlled clinical trial

BACKGROUND

The mechanistic effects of gamma transcranial alternating current stimulation (tACS) on hippocampal gamma oscillation activity in Alzheimer's Disease (AD) remains unclear. This study aimed to clarify beneficial effects of gamma tACS on cognitive functioning in AD and to elucidate effects on hippocampal gamma oscillation activity.

METHODS

This is a double-blind, randomized controlled single-center trial. Participants with mild AD were randomized to tACS group or sham group, and underwent 30 one-hour sessions of either 40 Hz tACS or sham stimulation over consecutive 15 days. Cognitive functioning, structural magnetic resonance imaging (MRI), and simultaneous electroencephalography-functional MRI (EEG-fMRI) were evaluated at baseline, the end of the intervention and at 3-month follow-up from the randomization.

RESULTS

A total of 46 patients were enrolled (23 in the tACS group, 23 in the sham group). There were no group differences in the change of the primary outcome, 11-item cognitive subscale of the Alzheimer's Disease Assessment Scale (ADAS-Cog) score after intervention (group*time, p = 0.449). For secondary outcomes, compared to the control group, the intervention group showed significant improvement in MMSE (group*time, p = 0.041) and MoCA scores (non-parametric test, p = 0.025), which were not sustained at 3-month follow-up. We found an enhancement of theta-gamma coupling in the hippocampus, which was positively correlated with improvements of MMSE score and delayed recall. Additionally, fMRI revealed increase of the local neural activity in the hippocampus.

CONCLUSION

Effects on the enhancement of theta-gamma coupling and neural activity within the hippocampus suggest mechanistic models for potential therapeutic mechanisms of tACS.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT03920826; Registration Date: 2019-04-19.

Role of stress and early-life stress in the pathogeny of inflammatory bowel disease

Numerous preclinical and clinical studies have shown that stress is one of the main environmental factor playing a significant role in the pathogeny and life-course of bowel diseases. However, stressful events that occur early in life, even during the fetal life, leave different traces within the central nervous system, in area involved in stress response and autonomic network but also in emotion, cognition and memory regulation. Early-life stress can disrupt the prefrontal-amygdala circuit thus favoring an imbalance of the autonomic nervous system and the hypothalamic-pituitary adrenal axis, resulting in anxiety-like behaviors. The down regulation of vagus nerve and cholinergic anti-inflammatory pathway favors pro-inflammatory conditions. Recent data suggest that emotional abuse at early life are aggravating risk factors in inflammatory bowel disease. This review aims to unravel the mechanisms that explain the consequences of early life events and stress in the pathophysiology of inflammatory bowel disease and their mental co-morbidities. A review of therapeutic potential will also be covered.

A bibliometric analysis of transcranial alternating current stimulation

Background

Transcranial alternating current stimulation (tACS) can apply currents of varying intensity to the scalp, modulating cortical excitability and brain activity. tACS is a relatively new neuromodulation intervention that is now widely used in clinical practice. Many papers related to tACS have been published in various journals. However, there are no articles that objectively and directly introduce the development trend and research hotspots of tACS. Therefore, the aim of this study is to use CiteSpace to visually analyze the recent tACS-related publications, systematically and in detail summarize the current research hotspots and trends in this field, and provide valuable information for future tACS-related research.

Material and methods

The database Web of Science Core Collection Science Citation Index Expanded was used and searched from build to 4 August 2023. Using the CiteSpace to analyze the authors, institutions, countries, keywords, co-cited authors, journals, and references.

Results

A total of 677 papers were obtained. From 2008 to 2023, the number of publications shows an increasing trend, albeit with some fluctuations. The most productive country in this field was Germany. The institution with the highest number of publications is Carl von Ossietzky University of Oldenburg (n = 50). According to Bradford's law, 7 journals are considered core journals in the field. Herrmann, CS was the author with the most publications (n = 40), while Antal, A was the author with the highest number of co-citations (n = 391) and betweenness centrality (n = 0.16). Disease, neural mechanisms of the brain and electric stimulation are the major research areas in the field. The effect of tACS in different diseases, multi-site stimulation, combined treatment and evaluation are the future research hotspots and trends.

Conclusion

tACS has research value and research potential, and more and more researchers are paying attention to it. The findings of this bibliometric study provide the current status and trends in the clinical research of tACS and may help researchers to identify hotspots s and explore new research directions in this field.

Transcranial alternating current stimulation improves quality of life in Parkinson's disease: study protocol for a randomized, double-blind, controlled trial

BACKGROUND

The neural cells in the brains of patients with Parkinson's disease (PWP) display aberrant synchronized oscillatory activity within the beta frequency range. Additionally, enhanced gamma oscillations may serve as a compensatory mechanism for motor inhibition mediated by beta activity and also reinstate plasticity in the primary motor cortex affected by Parkinson's disease. Transcranial alternating current stimulation (tACS) can synchronize endogenous oscillations with exogenous rhythms, thereby modulating cortical activity. The objective of this study is to investigate whether the addition of tACS to multidisciplinary intensive rehabilitation treatment (MIRT) can improve symptoms of PWP so as to enhance the quality of life in individuals with Parkinson's disease based on the central-peripheral-central theory.

METHODS

The present study was a randomized, double-blind trial that enrolled 60 individuals with Parkinson's disease aged between 45 and 70 years, who had Hoehn-Yahr scale scores ranging from 1 to 3. Participants were randomly assigned in a 1:1 ratio to either the tACS + MIRT group or the sham-tACS + MIRT group. The trial consisted of a two-week double-blind treatment period followed by a 24-week follow-up period, resulting in a total duration of twenty-six weeks. The primary outcome measured the change in PDQ-39 scores from baseline (T0) to 4 weeks (T2), 12 weeks (T3), and 24 weeks (T4) after completion of the intervention. The secondary outcome assessed changes in MDS-UPDRS III scores at T0, the end of intervention (T1), T2, T3, and T4. Additional clinical assessments and mechanistic studies were conducted as tertiary outcomes.

DISCUSSION

The objective of this study is to demonstrate that tACS can enhance overall functionality and improve quality of life in PWP, based on the framework of MIRT. Additionally, it seeks to establish a potential correlation between these therapeutic effects and neuroplasticity alterations in relevant brain regions. The efficacy of tACS will be assessed during the follow-up period in order to optimize neuroplasticity and enhance its potential impact on rehabilitation efficiency for PWP.

TRIAL REGISTRATION

Chinese Clinical Trial Registry ChiCTR2300071969. Registered on 30 May 2023.

Brain-wide activation involved in 15 mA transcranial alternating current stimulation in patients with first-episode major depressive disorder

Background

Although 15 mA transcranial alternating current stimulation (tACS) has a therapeutic effect on depression, the activations of brain structures in humans accounting for this tACS configuration remain largely unknown.

Aims

To investigate which intracranial brain structures are engaged in the tACS at 77.5 Hz and 15 mA, delivered via the forehead and the mastoid electrodes in the human brain.

Methods

Actual human head models were built using the magnetic resonance imagings of eight outpatient volunteers with drug-naïve, first-episode major depressive disorder and then used to perform the electric field distributions with SimNIBS software.

Results

The electric field distributions of the sagittal, coronal and axial planes showed that the bilateral frontal lobes, bilateral temporal lobes, hippocampus, cingulate, hypothalamus, thalamus, amygdala, cerebellum and brainstem were visibly stimulated by the 15 mA tACS procedure.

Conclusions

Brain-wide activation, including the cortex, subcortical structures, cerebellum and brainstem, is involved in the 15 mA tACS intervention for first-episode major depressive disorder. Our results indicate that the simultaneous involvement of multiple brain regions is a possible mechanism for its effectiveness in reducing depressive symptoms.

Mood and microbes: a comprehensive review of intestinal microbiota's impact on depression

Depression is considered a multifaceted and intricate mental disorder of growing concern due to its significant impact on global health issues. The human gut microbiota, also known as the "second brain," has an important role in the CNS by regulating it through chemical, immunological, hormonal, and neurological processes. Various studies have found a significant bidirectional link between the brain and the gut, emphasizing the onset of depression therapies. The biological and molecular processes underlying depression and microbiota are required, as the bidirectional association may represent a novel study. However, profound insights into the stratification and diversity of the gut microbiota are still uncommon. This article investigates the emerging evidence of a bacterial relationship between the gut and the brain's neurological system and its potential pathogenicity and relevance. The interplay of microbiota, immune system, nervous system neurotransmitter synthesis, and neuroplasticity transitions is also widely studied. The consequences of stress, dietary fibers, probiotics, prebiotics, and antibiotics on the GB axis are being studied. Multiple studies revealed the processes underlying this axis and led to the development of effective microbiota-based drugs for both prevention and treatment. Therefore, the results support the hypothesis that gut microbiota influences depression and provide a promising area of research for an improved knowledge of the etiology of the disease and future therapies.

Altered Neuronal Activity Patterns of the Prefrontal Cortex in Alzheimer's Disease After Transcranial Alternating Current Stimulation: A Resting-State fMRI Study

Background

Transcranial alternating current stimulation (tACS) could improve cognition in patients with Alzheimer's disease (AD). However, the effects of tACS on brain activity remain unclear.

Objective

The purpose is to investigate the change in regional neuronal activity after tACS in AD patients employing resting-state functional magnetic resonance imaging (rs-fMRI).

Methods

A total of 46 patients with mild AD were enrolled. Each patient received 30 one-hour sessions of real or sham tACS for three weeks (clinical trial: NCT03920826). The fractional amplitude of low-frequency fluctuations (fALFF) and the regional homogeneity (ReHo) measured by rs-fMRI were calculated to evaluate the regional brain activity.

Results

Compared to baseline, AD patients in the real group exhibited increased fALFF in the left middle frontal gyrus-orbital part and right inferior frontal gyrus-orbital part, as well as increased ReHo in the left precentral gyrus and right middle frontal gyrus at the end of intervention. At the 3-month follow-up, fALFF increased in the left superior parietal lobule and right inferior temporal gyrus, as well as ReHo, in the left middle frontal gyrus and right superior medial frontal gyrus. A higher fALFF in the right lingual gyrus and ReHo in the right parahippocampal gyrus were observed in the response group than in the nonresponse group.

Conclusions

The findings demonstrated the beneficial effects of tACS on the neuronal activity of the prefrontal cortex and even more extensive regions and provided a neuroimaging biomarker of treatment response in AD patients.

Hemophilia Healing with AAV: Navigating the Frontier of Gene Therapy

Gene therapy for hemophilia has advanced tremendously after thirty years of continual study and development. Advancements in medical science have facilitated attaining normal levels of Factor VIII (FVIII) or Factor IX (FIX) in individuals with haemophilia, thereby offering the potential for their complete recovery. Despite the notable advancements in various countries, there is significant scope for further enhancement in haemophilia gene therapy. Adeno-associated virus (AAV) currently serves as the primary vehicle for gene therapy in clinical trials targeting haemophilia. Subsequent investigations will prioritize enhancing viral capsid structures, transgene compositions, and promoters to achieve heightened transduction efficacy, diminished immunogenicity, and more predictable therapeutic results. The present study indicates that whereas animal models have transduction efficiency that is over 100% high, human hepatocytes are unable to express clotting factors and transduction efficiency to comparable levels. According to the current study, achieving high transduction efficiency and high levels of clotting factor expression in human hepatocytes is still insufficient. It is also crucial to reduce the risk of cellular stress caused by protein overload. Despite encountering various hurdles, the field of haemophilia gene therapy holds promise for the future. As technology continues to advance and mature, it is anticipated that a personalized therapeutic approach will be developed to cure haemophilia effectively.

MLP-RL-CRD: diagnosis of cardiovascular risk in athletes using a reinforcement learning-based multilayer perceptron

Objective.Pre-participation medical screening of athletes is necessary to pinpoint individuals susceptible to cardiovascular events.Approach.The article presents a reinforcement learning (RL)-based multilayer perceptron, termed MLP-RL-CRD, designed to detect cardiovascular risk among athletes. The model underwent training using a publicized dataset that included the anthropological measurements (such as height and weight) and biomedical metrics (covering blood pressure and pulse rate) of 26 002 athletes. To address the data imbalance, a novel RL-based technique was adopted. The problem was framed as a series of sequential decisions in which an agent classified a received instance and received a reward at each level. To resolve the insensitivity to the initialization of conventional gradient-based learning methods, a mutual learning-based artificial bee colony (ML-ABC) was proposed.Main Results.The model outcomes were validated against positive (P) and negative (N) ECG findings that had been labeled by experts to signify individuals 'at risk' and 'not at risk,' respectively. The MLP-RL-CRD approach achieves superior outcomes (F-measure 87.4%; geometric mean 89.6%) compared with other deep models and traditional machine learning techniques. Optimal values for crucial parameters, including the reward function, were identified for the model based on experiments on the study dataset. Ablation studies, which omitted elements of the suggested model, affirmed the autonomous, positive, stepwise influence of these components on performing the model.Significance.This study introduces a novel, effective method for early cardiovascular risk detection in athletes, merging reinforcement learning and multilayer perceptrons, advancing medical screening and predictive healthcare. The results could have far-reaching implications for athlete health management and the broader field of predictive healthcare analytics.

Biooriented Synthesis of Ibuprofen-Clubbed Novel Bis-Schiff Base Derivatives as Potential Hits for Malignant Glioma: In Vitro Anticancer Activity and In Silico Approach

This research work is based on the synthesis of bis-Schiff base derivatives of the commercially available ibuprofen drug in outstanding yields through multistep reactions. Structures of the synthesized compounds were confirmed by the help of modern spectroscopic techniques including high-resolution electrospray ionization mass spectrometry (HR-ESI-MS), 1H NMR, and 13C NMR. The synthesized compounds were evaluated for their anticancer activity using a normal human embryonic kidney HEK293 cell and U87-malignant glioma (ATCC-HTB-14) as a cancer cell line. All of the synthesized compounds among the series exhibited excellent to less antiproliferative activity having IC50 values ranging from 5.75 ± 0.43 to 150.45 ± 0.20 μM. Among them, compound 5e (IC50 = 5.75 ± 0.43 μM) was found as the most potent antiprolifarative agent, while 5f, 5b, 5a, 5n, 5r, 5s, 5g, 5q, 5i, and 5j exhibited good activity with IC50 values from 24.17 ± 0.46 to 43.71 ± 0.07 μM. These findings suggest that these cells (HEK293) are less cytotoxic to the activities of compounds and increase the cancer cell death in brain, while the lower cytotoxicity of the potent compounds in noncancerous cells suggests that these derivatives will provide promising treatment for patients suffering from brain cancer. The results of the docking study exposed a promising affinity of the active compounds toward casein kinase-2 enzyme, which shows green signal for cancer treatment.

Deeper insight into ferroptosis: association with Alzheimer's, Parkinson's disease, and brain tumors and their possible treatment by nanomaterials induced ferroptosis

Ferroptosis is an emerging and novel type of iron-dependent programmed cell death which is mainly caused by the excessive deposition of free intracellular iron in the brain cells. This deposited free iron exerts a ferroptosis pathway, resulting in lipid peroxidation (LiPr). There are mainly three ferroptosis pathways viz. iron metabolism-mediated cysteine/glutamate, and LiPr-mediated. Iron is required by the brain as a redox metal for several physiological activities. Due to the iron homeostasis balance disruption, the brain gets adversely affected which further causes neurodegenerative diseases (NDDs) like Parkinson's and Alzheimer's disease, strokes, and brain tumors like glioblastoma (GBS), and glioma. Nanotechnology has played an important role in the prevention and treatment of these NDDs. A synergistic effect of nanomaterials and ferroptosis could prove to be an effective and efficient approach in the field of nanomedicine. In the current review, the authors have highlighted all the latest research in the field of ferroptosis, specifically emphasizing on the role of major molecular key players and various mechanisms involved in the ferroptosis pathway. Moreover, here the authors have also addressed the correlation of ferroptosis with the pathophysiology of NDDs and theragnostic effect of ferroptosis and nanomaterials for the prevention and treatment of NDDs.

Usage of computational method for hemodynamic analysis of intracranial aneurysm rupture risk in different geometrical aspects

The importance of the parent vessel geometrical feature on the risk of cerebral aneurysm rupture is unavoidable. This study presents inclusive details on the hemodynamics of Internal carotid artery (ICA) aneurysms with different parent vessel mean diameters. Different aspects of blood hemodynamics are compared to find a reasonable connection between parent vessel mean diameter and significant hemodynamic factors of wall shear stress (WSS), oscillatory shear index (OSI), and pressure distribution. To access hemodynamic data, computational fluid dynamics is used to model the blood stream inside the cerebral aneurysms. A hemodynamic comparison of the selected cerebral aneurysm shows that the minimum WSS is reduced by about 71% as the parent vessel's mean diameter is increased from 3.18 to 4.48 mm.

Influence of parent vessel feature on the risk of internal carotid artery aneurysm rupture via computational method

In this study, the role of sac section area and parent vessel diameter on the hemodynamic feature of the blood flow in selected internal carotid artery (ICA) aneurysms is comprehensively investigated. The changes of wall shear stress, pressure, and oscillatory shear index (OSI) of blood stream on the vessel for various aneurysms with coiling treatment. To attain hemodynamic factors, computational technique is used for the modeling of non-Newtonian transient blood flow inside the three different ICA aneurysms. Three different saccular models with various Parent vessel mean Diameter is investigated in this study. The achieved outcomes show that increasing the diameter of the parent vessel directly decreases the OSI value on the sac surface. In addition, the mean wall shear stress decreases with the increase of the parent vessel diameter.

Computational and statistical analyses of blood hemodynamic inside cerebral aneurysms for treatment evaluation of endovascular coiling

Diagnosis of aneurysm and possibility of aneurysm rupture are crucial for avoiding brain hemorrhage. In this work, blood stream inside internal carotid arteries (ICAs) are simulated in diverse working conditions to disclose the importance of hemodynamic factors on the rupture of aneurysm. The main attention of this study is to investigate the role of hemodynamic on the aneurysm rupture. Statistical and computational methods are applied to investigate coiling porosity and blood hematocrit in 9 specific real ICA geometries. Response surface model (RSM) develops 25 runs to investigate all features of selected geometrical parameters and treatment factors. Computational fluid dynamic is used for the simulation of the blood stream in the selected aneurysms. The effects of sac section area and mean radius of parent vessel on blood hemodynamics are fully investigated. Hemodynamic factors are examined and compared at the peak systolic time instant, including pressure distributions, and velocity. Achieved results indicate that the increasing sac section area (from 36.6 to 75.4 mm2) results in 20% pressure reduction on the sac wall.

A novel automated Parkinson's disease identification approach using deep learning and EEG

The neurological ailment known as Parkinson's disease (PD) affects people throughout the globe. The neurodegenerative PD-related disorder primarily affects people in middle to late life. Motor symptoms such as tremors, muscle rigidity, and sluggish, clumsy movement are common in patients with this disorder. Genetic and environmental variables play significant roles in the development of PD. Despite much investigation, the root cause of this neurodegenerative disease is still unidentified. Clinical diagnostics rely heavily on promptly detecting such irregularities to slow or stop the progression of illnesses successfully. Because of its direct correlation with brain activity, electroencephalography (EEG) is an essential PD diagnostic technique. Electroencephalography, or EEG, data are biomarkers of brain activity changes. However, these signals are non-linear, non-stationary, and complicated, making analysis difficult. One must often resort to a lengthy human labor process to accomplish results using traditional machine-learning approaches. The breakdown, feature extraction, and classification processes are typical examples of these stages. To overcome these obstacles, we present a novel deep-learning model for the automated identification of Parkinson's disease (PD). The Gabor transform, a standard method in EEG signal processing, was used to turn the raw data from the EEG recordings into spectrograms. In this research, we propose densely linked bidirectional long short-term memory (DLBLSTM), which first represents each layer as the sum of its hidden state plus the hidden states of all layers above it, then recursively transmits that representation to all layers below it. This study's suggested deep learning model was trained using these spectrograms as input data. Using a robust sixfold cross-validation method, the proposed model showed excellent accuracy with a classification accuracy of 99.6%. The results indicate that the suggested algorithm can automatically identify PD.

Trends in using deep learning algorithms in biomedical prediction systems

In the domain of using DL-based methods in medical and healthcare prediction systems, the utilization of state-of-the-art deep learning (DL) methodologies assumes paramount significance. DL has attained remarkable achievements across diverse domains, rendering its efficacy particularly noteworthy in this context. The integration of DL with health and medical prediction systems enables real-time analysis of vast and intricate datasets, yielding insights that significantly enhance healthcare outcomes and operational efficiency in the industry. This comprehensive literature review systematically investigates the latest DL solutions for the challenges encountered in medical healthcare, with a specific emphasis on DL applications in the medical domain. By categorizing cutting-edge DL approaches into distinct categories, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), long short-term memory (LSTM) models, support vector machine (SVM), and hybrid models, this study delves into their underlying principles, merits, limitations, methodologies, simulation environments, and datasets. Notably, the majority of the scrutinized articles were published in 2022, underscoring the contemporaneous nature of the research. Moreover, this review accentuates the forefront advancements in DL techniques and their practical applications within the realm of medical prediction systems, while simultaneously addressing the challenges that hinder the widespread implementation of DL in image segmentation within the medical healthcare domains. These discerned insights serve as compelling impetuses for future studies aimed at the progressive advancement of using DL-based methods in medical and health prediction systems. The evaluation metrics employed across the reviewed articles encompass a broad spectrum of features, encompassing accuracy, precision, specificity, F-score, adoptability, adaptability, and scalability.

Medical image analysis using deep learning algorithms

In the field of medical image analysis within deep learning (DL), the importance of employing advanced DL techniques cannot be overstated. DL has achieved impressive results in various areas, making it particularly noteworthy for medical image analysis in healthcare. The integration of DL with medical image analysis enables real-time analysis of vast and intricate datasets, yielding insights that significantly enhance healthcare outcomes and operational efficiency in the industry. This extensive review of existing literature conducts a thorough examination of the most recent deep learning (DL) approaches designed to address the difficulties faced in medical healthcare, particularly focusing on the use of deep learning algorithms in medical image analysis. Falling all the investigated papers into five different categories in terms of their techniques, we have assessed them according to some critical parameters. Through a systematic categorization of state-of-the-art DL techniques, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Long Short-term Memory (LSTM) models, and hybrid models, this study explores their underlying principles, advantages, limitations, methodologies, simulation environments, and datasets. Based on our results, Python was the most frequent programming language used for implementing the proposed methods in the investigated papers. Notably, the majority of the scrutinized papers were published in 2021, underscoring the contemporaneous nature of the research. Moreover, this review accentuates the forefront advancements in DL techniques and their practical applications within the realm of medical image analysis, while simultaneously addressing the challenges that hinder the widespread implementation of DL in image analysis within the medical healthcare domains. These discerned insights serve as compelling impetuses for future studies aimed at the progressive advancement of image analysis in medical healthcare research. The evaluation metrics employed across the reviewed articles encompass a broad spectrum of features, encompassing accuracy, sensitivity, specificity, F-score, robustness, computational complexity, and generalizability.

Impacts of morphology parameters on the risk of rupture in intracranial aneurysms: statistical and computational analyses

The hemodynamic analysis of the blood stream inside the cerebral aneurysms reveals the risk of the aneurysm rupture. In addition, the high risk region prone to rupture would be determined by the hemodynamic analysis of the blood. In present article, computational fluid dynamic is used for the investigation of the hemodynamic effects on the aneurysm wall and risk of rupture. This study tries to find the connection between the risk of rupture with three geometrical features of aneurysm i.e., Ellipsoid Max semi-axis, Size ratio and Tortuosity. Statistical analysis is done over 30 different ruptured /unruptured ICA aneurysms to find meaningful relation between selected geometrical factors and rupture risk. The hemodynamic analysis is done over four distinct aneurysm models to attain more details on effects of chosen geometrical factors. The results of simulations indicate that the Ellipsoid Max semi-axis have meaningful impacts on the risk of rupture.